{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
     "start_time": "2025-02-06T14:04:28.531690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:01.888413Z",
     "iopub.status.busy": "2025-02-07T11:34:01.888259Z",
     "iopub.status.idle": "2025-02-07T11:34:04.455032Z",
     "shell.execute_reply": "2025-02-07T11:34:04.454377Z",
     "shell.execute_reply.started": "2025-02-07T11:34:01.888391Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.456752Z",
     "iopub.status.busy": "2025-02-07T11:34:04.456234Z",
     "iopub.status.idle": "2025-02-07T11:34:04.459579Z",
     "shell.execute_reply": "2025-02-07T11:34:04.459013Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.456721Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.460479Z",
     "iopub.status.busy": "2025-02-07T11:34:04.460303Z",
     "iopub.status.idle": "2025-02-07T11:34:04.468636Z",
     "shell.execute_reply": "2025-02-07T11:34:04.468038Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.460462Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.469489Z",
     "iopub.status.busy": "2025-02-07T11:34:04.469308Z",
     "iopub.status.idle": "2025-02-07T11:34:04.472121Z",
     "shell.execute_reply": "2025-02-07T11:34:04.471586Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.469472Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.473691Z",
     "iopub.status.busy": "2025-02-07T11:34:04.473432Z",
     "iopub.status.idle": "2025-02-07T11:34:04.498827Z",
     "shell.execute_reply": "2025-02-07T11:34:04.498316Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.473671Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1131971.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.499968Z",
     "iopub.status.busy": "2025-02-07T11:34:04.499536Z",
     "iopub.status.idle": "2025-02-07T11:34:04.508071Z",
     "shell.execute_reply": "2025-02-07T11:34:04.507545Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.499948Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.508918Z",
     "iopub.status.busy": "2025-02-07T11:34:04.508687Z",
     "iopub.status.idle": "2025-02-07T11:34:04.511298Z",
     "shell.execute_reply": "2025-02-07T11:34:04.510835Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.508901Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.512299Z",
     "iopub.status.busy": "2025-02-07T11:34:04.511931Z",
     "iopub.status.idle": "2025-02-07T11:34:04.517876Z",
     "shell.execute_reply": "2025-02-07T11:34:04.517393Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.512280Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1dd026c49a90114",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.363561Z",
     "start_time": "2025-02-06T14:04:29.355876Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.518886Z",
     "iopub.status.busy": "2025-02-07T11:34:04.518576Z",
     "iopub.status.idle": "2025-02-07T11:34:04.525791Z",
     "shell.execute_reply": "2025-02-07T11:34:04.525351Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.518869Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71ae51fb9433db98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.444646Z",
     "start_time": "2025-02-06T14:04:29.441583Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.526770Z",
     "iopub.status.busy": "2025-02-07T11:34:04.526372Z",
     "iopub.status.idle": "2025-02-07T11:34:04.529159Z",
     "shell.execute_reply": "2025-02-07T11:34:04.528695Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.526752Z"
    }
   },
   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24e99727ef49605",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.533317Z",
     "start_time": "2025-02-06T14:04:29.526671Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.529829Z",
     "iopub.status.busy": "2025-02-07T11:34:04.529687Z",
     "iopub.status.idle": "2025-02-07T11:34:04.533946Z",
     "shell.execute_reply": "2025-02-07T11:34:04.533550Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.529814Z"
    }
   },
   "outputs": [],
   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.571687Z",
     "start_time": "2025-02-06T14:04:29.566838Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.534864Z",
     "iopub.status.busy": "2025-02-07T11:34:04.534456Z",
     "iopub.status.idle": "2025-02-07T11:34:04.537368Z",
     "shell.execute_reply": "2025-02-07T11:34:04.536959Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.534848Z"
    }
   },
   "outputs": [],
   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "847a70137968848a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.819755Z",
     "start_time": "2025-02-06T14:04:29.650702Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.538138Z",
     "iopub.status.busy": "2025-02-07T11:34:04.537981Z",
     "iopub.status.idle": "2025-02-07T11:34:04.682407Z",
     "shell.execute_reply": "2025-02-07T11:34:04.681719Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.538123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.685788Z",
     "iopub.status.busy": "2025-02-07T11:34:04.685439Z",
     "iopub.status.idle": "2025-02-07T11:34:04.861314Z",
     "shell.execute_reply": "2025-02-07T11:34:04.860525Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.685763Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.862342Z",
     "iopub.status.busy": "2025-02-07T11:34:04.862083Z",
     "iopub.status.idle": "2025-02-07T11:34:04.868802Z",
     "shell.execute_reply": "2025-02-07T11:34:04.868201Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.862321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.869808Z",
     "iopub.status.busy": "2025-02-07T11:34:04.869540Z",
     "iopub.status.idle": "2025-02-07T11:34:04.876080Z",
     "shell.execute_reply": "2025-02-07T11:34:04.875535Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.869786Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1128, 0.5105, 0.3767],\n",
      "        [0.3374, 0.3174, 0.3452]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:04.877006Z",
     "iopub.status.busy": "2025-02-07T11:34:04.876760Z",
     "iopub.status.idle": "2025-02-07T11:34:05.116247Z",
     "shell.execute_reply": "2025-02-07T11:34:05.115433Z",
     "shell.execute_reply.started": "2025-02-07T11:34:04.876987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:05.117651Z",
     "iopub.status.busy": "2025-02-07T11:34:05.117335Z",
     "iopub.status.idle": "2025-02-07T11:34:05.355621Z",
     "shell.execute_reply": "2025-02-07T11:34:05.354933Z",
     "shell.execute_reply.started": "2025-02-07T11:34:05.117628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:05.356815Z",
     "iopub.status.busy": "2025-02-07T11:34:05.356511Z",
     "iopub.status.idle": "2025-02-07T11:34:05.367329Z",
     "shell.execute_reply": "2025-02-07T11:34:05.366542Z",
     "shell.execute_reply.started": "2025-02-07T11:34:05.356793Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:05.368425Z",
     "iopub.status.busy": "2025-02-07T11:34:05.368214Z",
     "iopub.status.idle": "2025-02-07T11:34:05.374625Z",
     "shell.execute_reply": "2025-02-07T11:34:05.374003Z",
     "shell.execute_reply.started": "2025-02-07T11:34:05.368406Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:05.375655Z",
     "iopub.status.busy": "2025-02-07T11:34:05.375400Z",
     "iopub.status.idle": "2025-02-07T11:34:05.382656Z",
     "shell.execute_reply": "2025-02-07T11:34:05.381957Z",
     "shell.execute_reply.started": "2025-02-07T11:34:05.375636Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:05.383880Z",
     "iopub.status.busy": "2025-02-07T11:34:05.383372Z",
     "iopub.status.idle": "2025-02-07T11:34:05.389366Z",
     "shell.execute_reply": "2025-02-07T11:34:05.388769Z",
     "shell.execute_reply.started": "2025-02-07T11:34:05.383858Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:05.390483Z",
     "iopub.status.busy": "2025-02-07T11:34:05.390239Z",
     "iopub.status.idle": "2025-02-07T11:34:05.541655Z",
     "shell.execute_reply": "2025-02-07T11:34:05.541096Z",
     "shell.execute_reply.started": "2025-02-07T11:34:05.390460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:05.542665Z",
     "iopub.status.busy": "2025-02-07T11:34:05.542397Z",
     "iopub.status.idle": "2025-02-07T11:34:06.041779Z",
     "shell.execute_reply": "2025-02-07T11:34:06.041088Z",
     "shell.execute_reply.started": "2025-02-07T11:34:05.542645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.042836Z",
     "iopub.status.busy": "2025-02-07T11:34:06.042579Z",
     "iopub.status.idle": "2025-02-07T11:34:06.059923Z",
     "shell.execute_reply": "2025-02-07T11:34:06.059295Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.042817Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.060831Z",
     "iopub.status.busy": "2025-02-07T11:34:06.060591Z",
     "iopub.status.idle": "2025-02-07T11:34:06.065701Z",
     "shell.execute_reply": "2025-02-07T11:34:06.065033Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.060809Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.066681Z",
     "iopub.status.busy": "2025-02-07T11:34:06.066464Z",
     "iopub.status.idle": "2025-02-07T11:34:06.070855Z",
     "shell.execute_reply": "2025-02-07T11:34:06.070147Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.066663Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.071745Z",
     "iopub.status.busy": "2025-02-07T11:34:06.071525Z",
     "iopub.status.idle": "2025-02-07T11:34:06.176689Z",
     "shell.execute_reply": "2025-02-07T11:34:06.175812Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.071726Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.177797Z",
     "iopub.status.busy": "2025-02-07T11:34:06.177528Z",
     "iopub.status.idle": "2025-02-07T11:34:06.182791Z",
     "shell.execute_reply": "2025-02-07T11:34:06.182040Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.177778Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.183790Z",
     "iopub.status.busy": "2025-02-07T11:34:06.183554Z",
     "iopub.status.idle": "2025-02-07T11:34:06.189621Z",
     "shell.execute_reply": "2025-02-07T11:34:06.188908Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.183768Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"nums_layers_4.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.190634Z",
     "iopub.status.busy": "2025-02-07T11:34:06.190394Z",
     "iopub.status.idle": "2025-02-07T11:34:06.195258Z",
     "shell.execute_reply": "2025-02-07T11:34:06.194531Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.190611Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.196234Z",
     "iopub.status.busy": "2025-02-07T11:34:06.196003Z",
     "iopub.status.idle": "2025-02-07T11:34:06.200688Z",
     "shell.execute_reply": "2025-02-07T11:34:06.200210Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.196215Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.201708Z",
     "iopub.status.busy": "2025-02-07T11:34:06.201468Z",
     "iopub.status.idle": "2025-02-07T11:34:06.210323Z",
     "shell.execute_reply": "2025-02-07T11:34:06.209643Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.201688Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.211369Z",
     "iopub.status.busy": "2025-02-07T11:34:06.211092Z",
     "iopub.status.idle": "2025-02-07T11:34:06.525140Z",
     "shell.execute_reply": "2025-02-07T11:34:06.524598Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.211350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 4, # 可调整\n",
    "    \"num_encoder_layers\": 4, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:06.525977Z",
     "iopub.status.busy": "2025-02-07T11:34:06.525751Z",
     "iopub.status.idle": "2025-02-07T11:34:07.191819Z",
     "shell.execute_reply": "2025-02-07T11:34:07.191347Z",
     "shell.execute_reply.started": "2025-02-07T11:34:06.525961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 57237695\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T11:34:07.192639Z",
     "iopub.status.busy": "2025-02-07T11:34:07.192396Z",
     "iopub.status.idle": "2025-02-07T11:53:10.055842Z",
     "shell.execute_reply": "2025-02-07T11:53:10.055264Z",
     "shell.execute_reply.started": "2025-02-07T11:34:07.192622Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "31999it [19:01, 28.04it/s, epoch=29, loss=2.08, val_loss=2.96]                       "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 30 / global_step 32000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:53:10.057520Z",
     "iopub.status.busy": "2025-02-07T11:53:10.056982Z",
     "iopub.status.idle": "2025-02-07T11:53:10.226536Z",
     "shell.execute_reply": "2025-02-07T11:53:10.225867Z",
     "shell.execute_reply.started": "2025-02-07T11:53:10.057494Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/nums_layers_4.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:53:10.227610Z",
     "iopub.status.busy": "2025-02-07T11:53:10.227303Z",
     "iopub.status.idle": "2025-02-07T11:53:21.004343Z",
     "shell.execute_reply": "2025-02-07T11:53:21.003597Z",
     "shell.execute_reply.started": "2025-02-07T11:53:10.227589Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "10it [00:00, 93.43it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "30it [00:00, 94.14it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:09, 98.29it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 2.9070081660219356\n",
      "testing bleu: 0.6921056725750646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:53:21.005380Z",
     "iopub.status.busy": "2025-02-07T11:53:21.005005Z",
     "iopub.status.idle": "2025-02-07T11:53:21.007825Z",
     "shell.execute_reply": "2025-02-07T11:53:21.007343Z",
     "shell.execute_reply.started": "2025-02-07T11:53:21.005360Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:53:21.008930Z",
     "iopub.status.busy": "2025-02-07T11:53:21.008553Z",
     "iopub.status.idle": "2025-02-07T11:53:21.489304Z",
     "shell.execute_reply": "2025-02-07T11:53:21.488651Z",
     "shell.execute_reply.started": "2025-02-07T11:53:21.008911Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:53:21.492998Z",
     "iopub.status.busy": "2025-02-07T11:53:21.492647Z",
     "iopub.status.idle": "2025-02-07T11:53:22.539605Z",
     "shell.execute_reply": "2025-02-07T11:53:22.539048Z",
     "shell.execute_reply.started": "2025-02-07T11:53:21.492974Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  8%|▊         | 10/128 [00:00<00:01, 69.83it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in a small white boat on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
